import pandas as pd 
from sklearn.preprocessing import MinMaxScaler
import pymysql
import pymysql.cursors
#李如欣
class MysqlUtils(object):
    def __init__(self):
        self.conn = pymysql.connect(
            host='127.0.0.1',
            user="root",
            passwd="root",
            db="scenic",
            port=3306,
            charset="utf8"
        )
        
    def is_holiday(self, date):
        """是否节假日判断
        """
        if date in ['2024-09-03', '2024-10-01', '2024-10-02', '2024-10-03', '2024-10-04', '2024-10-05', '2024-10-06', '2024-10-07', '2025-01-01', '2025-01-02','2025-01-03']:
            return 1
        return 0
    
    def get_scenic_data(self):
        """获取数据
        """
        cursor = self.conn.cursor(cursor=pymysql.cursors.DictCursor)
        
        sql = """
        SELECT DATE(g.create_time) as date, HOUR(g.create_time) as hour, count(*) as count FROM order_user_gate_rel g WHERE HOUR(g.create_time) BETWEEN 6 and 23 and DATE(g.create_time) < '2025-01-01' GROUP BY date, hour
        """
        
        cursor.execute(sql)
        ret = cursor.fetchall()

        df = pd.DataFrame(ret)
        # print(df.head)
        # 格式转换
        date_range = pd.date_range(start='2024-07-01', end='2024-12-31', freq='D')
        hours = range(6, 24)
        full_index = pd.MultiIndex.from_product([date_range, hours], names=['date', 'hour'])
        # print(full_index)
        df_full = df.set_index(['date', 'hour']).reindex(full_index, fill_value=0).reset_index()
        # 按天组织数据，每行包含18个小时的检票次数
        df_pivot = df_full.pivot(index='date', columns='hour', values='count')
        # print(df_pivot)
        df_pivot['dow'] = df_pivot.index.dayofweek # 星期几（0-6
        df_pivot['month'] = df_pivot.index.month # 月份
        # print(df_pivot)
        df_pivot['is_holiday'] = df_pivot.index.map(self.is_holiday)
        
        # 对星期几和月份进行独热编码
        df_pivot = pd.get_dummies(df_pivot, columns=['dow', 'month'], dtype=int)
        
        # 归一化小时检票列
        hours_columns = list(range(6, 24))
        df_hours = df_pivot[hours_columns].copy()
        
        feature_colunms = [col for col in df_pivot.columns if col not in hours_columns]
        df_feature = df_pivot[feature_colunms].copy()
        
        scaler = MinMaxScaler()
        scaled_hours = scaler.fit_transform(df_hours)
        from joblib import dump
        dump(scaler, 'NN/scaler.joblib')
        
        # 将归一化后的数据转化为DataFrame
        df_hours_scaled = pd.DataFrame(scaled_hours, columns=hours_columns, index=df_hours.index)
        
        # 合并
        df_pivot_clean = pd.concat([df_hours_scaled, df_feature], axis=1)
        print(df_pivot_clean)
        
        df_pivot_clean.to_csv('NN/scenic_data.csv', index=False)
        

if __name__ == '__main__':
    mu = MysqlUtils()
    mu.get_scenic_data()
    
    